IBM

About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Cache. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.

Molecular Electronics with Carbon Nanotubes

Abstract: Carbon nanotubes have unique properties that make them a most promising system on which to base molecular electronics. We briefly review the electrical characteristics of carbon nanotubes, and then focus on carbon nanotube field-effect transistors (CNTFETs). Procedures by which hole-transport, electron-transport and ambipolar CNTFETs can be fabricated are presented, and their electrical characteristics are discussed and compared with those of Si MOSFETs. Ways to fabricate arrays of CNTFETs are also demonstrated, and electron and hole CNTFETs are integrated to form complementary logic circuits.

Direct Observation and Analysis of Indentation Cracking in Glasses and Ceramics

Abstract: A review of the observations of indentation-induced fracture suggests that there is no simple generalization which may be made concerning crack initiation sequences. Here, we investigate the material dependence of the initiation sequence of indentation cracks (cone, radial, median, half-penny, and lateral) using an inverted tester allowing simultaneous viewing of the fracture process and measurement of the indeter load and displacement during contact. Two normal glasses, two anomalous glasses, and seven crystalline materials are examined. Key results include (i) direct evidence that the surface traces of cracks observed at indentation contacts are those of radial cracks, rather than median-nucleated half-penny cracks (at least for peak contact loads <40 N) and (ii) that, in crystalline materials, radial cracks form almost immediately on loading of the indenter, in anomalous glasses at somewhat greater loads, but in normal glasses during unloading. A detailed consideration of the stress fields arising during indentation contact predicts material-dependent initiation sequences, in agreement with observations, particularly those of radial crack formation on loading for materials with large modulus-to-hardness ratios. In addition, a new, unexplored crack system is demonstrated, the shallow lateral cracks, which appear to be responsible for material removal at sharp contacts.

Information diffusion through blogspace

Abstract: We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of weblogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.

Billing system for information dispersal system

Abstract: A billing process is disclosed for an information dispersal system or digital data storage system. The original data to be stored is separated into a number of data 'slices' or shares (22, 24, 26, 28, 30, and 32). These data subsets are stored on separate digital data storage devices (34, 36, 38, 40, 42, and 44) as a way of increasing privacy and security. A set of metadata tables are created, separate from the dispersed file share storage, to maintain information about the original data size of each block, file or set of file shares dispersed on the grid.

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

Abstract: How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence's representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.